Phase Detection with Hidden Markov Models for DVFS on Many-Core Processors

Abstract

The energy concerns of many-core processors are increasing with the number of cores. We provide a new method that reduces energy consumption of an application on many-core processors by identifying unique segments to apply dynamic voltage and frequency scaling (DVFS). Our method, phase-based voltage and frequency scaling (PVFS), hinges on the identification of phases, i.e., Segments of code with unique performance and power attributes, using hidden Markov Models. In particular, we demonstrate the use of this method to target hardware components on many-core processors such as Network-on-Chip (NoC). PVFS uses these phases to construct a static power schedule that uses DVFS to reduce energy with minimal performance penalty. This general scheme can be used with a variety of performance and power metrics to match the needs of the system and application. More importantly, the flexibility in the general scheme allows for targeting of the unique hardware components of future many-core processors. We provide an in-depth analysis of PVFS applied to five threaded benchmark applications, and demonstrate the advantage of using PVFS for 4 to 32 cores in a single socket. Empirical results of PVFS show a reduction of up to 10.1% of total energy while only impacting total time by at most 2.7% across all core counts. Furthermore, PVFS outperforms standard coarse-grain time-driven DVFS, while scaling better in terms of energy savings with increasing core counts.